Environment Selection

Use this cookbook when you know the benchmark and harness but need to choose where tasks should run.

Decision Table

Choose	When
`host_process`	The benchmark is lightweight, local, or service-backed.
`docker`	You need local container reproducibility.
`modal`	You want registry-image cloud sandboxes and provider-managed compute.
`daytona`	You want remote development-style sandboxes for coding or terminal tasks.

Docker Smoke Test

agentcompass run \
  swebench_verified \
  mini_swe_agent \
  glm-5.2 \
  --env docker \
  --benchmark-params '{"sample_ids":["astropy__astropy-12907"]}' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-chat \
  --task-concurrency 1

Remote Smoke Test

export DAYTONA_API_KEY="..."

agentcompass run \
  terminal_bench_2 \
  terminus2 \
  your-model \
  --env daytona \
  --benchmark-params '{"sample_ids":["overfull-hbox"]}' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY"

Image And Workspace Rules

For recipe-backed benchmarks, image and workspace are usually inferred from task metadata. For custom tasks, pass --env-params.

agentcompass run <benchmark> <harness> <model> --env docker --env-params '{"image":"python:3.13-slim","workspace":"/workspace"}'

Get Started

Key Modules

Environments

Cookbooks

Decision Table

Docker Smoke Test

Remote Smoke Test

Image And Workspace Rules

​Decision Table

​Docker Smoke Test

​Remote Smoke Test

​Image And Workspace Rules

​Related Pages

Decision Table

Docker Smoke Test

Remote Smoke Test

Image And Workspace Rules

Related Pages